Adaptive dimensional control based on in-cycle geometry monitoring and programming for CNC turning center

• Published in: International journal of advanced manufacturing technology 2011-08, Vol.55 (9-12), p.1079-1097
• Main Authors: Marinescu, Vasile, Constantin, Ionut, Apostu, Cristian, Marin, Florin Bogdan, Banu, Mihaela, Epureanu, Alexandru
• Format: Article
• Language: English
• Subjects: Adaptive algorithms; Adaptive control; Algorithms; CAE) and Design; Compensation; Computer-Aided Engineering (CAD; Decomposition; Deformation wear; Deviation; Engineering; Errors; Geometry; Industrial and Production Engineering; Mechanical Engineering; Media Management; Monitoring; Motion control; Numerical controls; Original Article; Positioning devices (machinery); Tool wear
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-010-3132-2

This paper presents a method to compensate the dimensional deviation, irrespective of the sources for its components, and to integrate the dimensional verification and dimensional control processes. Nowadays, approach in compensation of dimensional deviation is based on decomposing the deviation. The decomposing criterion is the error source such as positioning errors, thermal deformation, mechanical loads, tool wear, kinematical errors, dynamic force, and motion control. Then, one or even more components are modeled and compensated. On contrary, the proposed method is based on the decomposing of the tool path and consideration of the batch samples. The decomposition criteria ignores the error sources as: (1) speed of variation in space of the total deviation value for the tool path decomposition and (2) the speed of variation in time of the deviation model parameters values for batch samples decomposition. The data from the geometry holistic monitoring are used for both modeling and compensation of systematic component of the total error, also for checking the compliance with technical requirements. Two algorithms for processing of the data provided by geometry monitoring, namely the adaptive–predictive algorithm and adaptive–optimal algorithm, are presented. Nine experimental batches were machined to verify the efficiency of the proposed method using various model structures and processing algorithms. The results of method application have shown a reduction of deterministic and even nondeterministic part of the total error in what concern accuracy and precision. For the entire batch, the level of remanent error is less than 5% for deterministic part, and less than 75% for nondeterministic part. These results are clearly better than the other results reported; moreover, they refer to the whole processing error and entire batch.